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Pennino, Federico (2022) Deep learning based style transfer for low altitude aerial imagery. Master's, Universität Bielefeld.
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Abstract
Es wurde eine Simulationsumgebung entwickelt um parametrisiert synthetische Luftbildaufnahmen zum Training und Testen von Semantischen Neuronalen Netzen zu erstellen. Des weiteren wurde untersucht, ob generative adversarial networks genutzt werden können, um den Domain Gap zwischen echten und synthetischen Bildern zu verkleinern.
| Item URL in elib: | https://elib.dlr.de/193597/ | ||||||||
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| Document Type: | Thesis (Master's) | ||||||||
| Title: | Deep learning based style transfer for low altitude aerial imagery | ||||||||
| Authors: |
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| Date: | 2022 | ||||||||
| Refereed publication: | No | ||||||||
| Open Access: | Yes | ||||||||
| Number of Pages: | 78 | ||||||||
| Status: | Published | ||||||||
| Keywords: | GANs, UAV, Simualtion, Synthetic, Aerial Imagery | ||||||||
| Institution: | Universität Bielefeld | ||||||||
| Department: | Technische Fakultät | ||||||||
| HGF - Research field: | Aeronautics, Space and Transport | ||||||||
| HGF - Program: | Aeronautics | ||||||||
| HGF - Program Themes: | Components and Systems | ||||||||
| DLR - Research area: | Aeronautics | ||||||||
| DLR - Program: | L CS - Components and Systems | ||||||||
| DLR - Research theme (Project): | L - Unmanned Aerial Systems | ||||||||
| Location: | Köln-Porz | ||||||||
| Institutes and Institutions: | Institute of Software Technology Institute of Software Technology > Intelligent and Distributed Systems | ||||||||
| Deposited By: | Konen, Kai | ||||||||
| Deposited On: | 26 Jan 2023 11:10 | ||||||||
| Last Modified: | 26 Jan 2023 11:10 |
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